profiling-llm-cost

What problem does it solve?

This Skill finds where LLM application spend is coming from when bills spike, cache hit rates drop, or per-task economics are unknown. It replaces guesswork with a measured cost baseline so you can identify the real driver before changing prompts, models, or architecture.

Core Features & Use Cases

• Builds per-call cost from logged token counts and model pricing, including cached versus uncached input tokens and output tokens.
• Rolls costs up per task so agentic workflows can be evaluated by the economics a user actually experiences, not just by individual calls.
• Measures cache-hit trends, slices spend by model, step, route, or cohort, and compares the current window against a prior baseline to localize cost spikes.
• Produces ranked savings opportunities with the highest-leverage fix first, while refusing to recommend cost cuts without a measured baseline.
• Use it when a production chatbot, RAG pipeline, or multi-step agent suddenly costs more, when prompt caching appears to be failing, or when a team needs a budget gate before scaling.

Quick Start

Ask Claude to profile your LLM trace log, compute per-call and per-task cost, measure cache-hit rate, and identify the highest-leverage savings opportunities.